





def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:Optional[int]				)	->				List[Any]:
     '''simple docstring'''


     A_          =       [[] for _ in range(UpperCamelCase_				)]
     A_          =       key - 1

     if key <= 0:
          raise ValueError('''Height of grid can\'t be 0 or negative'''				)
     if key == 1 or len(UpperCamelCase_				) <= key:
          return input_string

     for position, character in enumerate(UpperCamelCase_				):
          A_          =       position % (lowest * 2)  # puts it in bounds
          A_          =       min(UpperCamelCase_  ,						lowest * 2 - num				)  # creates zigzag pattern
          temp_grid[num].append(UpperCamelCase_				)
     A_          =       [''''''.join(UpperCamelCase_				) for row in temp_grid]
     A_          =       ''''''.join(UpperCamelCase_				)

     return output_string

def      UpperCAmelCase_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Tuple				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       []
     A_          =       key - 1

     if key <= 0:
          raise ValueError('''Height of grid can\'t be 0 or negative'''				)
     if key == 1:
          return input_string

     A_          =       [[] for _ in range(UpperCamelCase_				)]  # generates template
     for position in range(len(UpperCamelCase_				)				):
          A_          =       position % (lowest * 2)  # puts it in bounds
          A_          =       min(UpperCamelCase_  ,						lowest * 2 - num				)  # creates zigzag pattern
          temp_grid[num].append('''*'''				)

     A_          =       0
     for row in temp_grid:  # fills in the characters
          A_          =       input_string[counter : counter + len(UpperCamelCase_				)]
          grid.append(list(UpperCamelCase_				)				)
          counter += len(UpperCamelCase_				)

     A_          =       ''''''  # reads as zigzag
     for position in range(len(UpperCamelCase_				)				):
          A_          =       position % (lowest * 2)  # puts it in bounds
          A_          =       min(UpperCamelCase_  ,						lowest * 2 - num				)  # creates zigzag pattern
          output_string += grid[num][0]
          grid[num].pop(0				)
     return output_string

def      UpperCAmelCase_    (    _UpperCAmelCase							:str				)	->				int:
     '''simple docstring'''


     A_          =       {}
     for key_guess in range(1  ,						len(UpperCamelCase_				)				):  # tries every key
          A_          =       decrypt(UpperCamelCase_  ,						UpperCamelCase_				)
     return results


if __name__ == "__main__":
      import doctest

      doctest.testmod()







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




